1. Field of the Invention
The present invention relates to a learning system, a learning method, and a robot apparatus, and is applicable to an entertainment robot, for example.
2. Description of the Related Art
Heretofore, for instance, in the case where a recognizer such as a fingerprint recognizer, a voiceprint recognizer in a security system performs the learning of a new category, the “learning mode” in which performing the learning is explicit to the user has been used.
In this “learning mode”, in the case of learning a fingerprint or a voiceprint for security as the above, the purpose that sensing information will be used for security is clear. Therefore, it is most preferable that the execution is explicit to make the user notice that information about himself/herself is being registered at present.
However, in an entertainment robot in that sensing information is used to identify that who is the user in the middle of an interaction, it is important that the user can be identified in natural interaction.
Therefore, in such entertainment robot, for instance, when in learning user's face, if the robot utters “I'll memorize your face. Please keep still.”, and that the above robot is executing the learning of user's face is explicitly shown to the user, it occurs a problem that natural interaction with the user, being the primary purpose, may be disturbed.
On the other hand, in an entertainment robot that performs the learning of user's name, to make the robot perform natural interaction with the user, an idea to make the robot memorize features of the user to be connected with the user's name learnt from the user (sensing information to be connected with the name) at one time, if possible, is necessary.
However, in entertainment robots provided heretofore, the determination of the success/failure of learning is inflexible as that if sufficient data cannot be obtained in a certain time, the learning is determined to be failure. Therefore, the frequency of failures of learning occurs in a dynamic environment, and sensing information is not easily connected with the name; as a result, there has been a problem that such an interaction annoying to the user that the robot asks the user his/her name many times occurs.
Furthermore, in the entertainment robots provided heretofore, in the case where the learning must be finished without obtaining sufficient learning data, the learning at the time is determined to be failure, and all the data obtained by that learning is abandoned. Therefore, the halfway learning result cannot be used; as a result, there has been a problem that efficient learning is difficult.
Accordingly, it can be considered that in an entertainment robot, if the user can be identified in natural interaction and the failures of learning can be lessened as well as possible, the entertainment activity can be further improved.